Title :
A study on the fault identification of transformers using the neural network
Author :
Yongli, Li ; Fuhai, Gu ; Jiali, He
Author_Institution :
Dept. of Electr. Eng., Tianjin Univ., China
Abstract :
A neural network method used to identify the different operating states of power transformers has been proposed and established. It is superior to the traditional transformer protective principles and can correctly identify, within a half cycle from the fault inception, the internal faults, magnetizing inrush current state, external faults and switching on the internal faults of a no-load transformer. In addition, this method has broad availability and high fault tolerant ability. A number of simulations have demonstrated its superiority
Keywords :
fault diagnosis; neural nets; power engineering computing; power transformers; availability; external faults; fault inception; fault switching; fault tolerant ability; internal faults; magnetizing inrush current state; neural network; no-load transformer; operating states; power transformer fault identification; simulation; Fault diagnosis; Magnetic switching; Neural networks; Power system harmonics; Power system relaying; Power system reliability; Power system simulation; Power transformers; Protective relaying; Surge protection;
Conference_Titel :
Power System Technology, 1998. Proceedings. POWERCON '98. 1998 International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4754-4
DOI :
10.1109/ICPST.1998.729247